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Proceedings Papers

A Comparative Study for Deep-Learning-Based Methods for Automated Reservoir Simulation

Alaa Maarouf, Sofiane Tahir, Shi Su, Chakib Kada Kloucha, Hussein Mustapha
Paper presented at the SPE Reservoir Characterisation and Simulation Conference and Exhibition, January 24–26, 2023
Paper Number: SPE-212594-MS
https://doi.org/10.2118/212594-MS
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View Paper titled, A Comparative Study for Deep-Learning-Based Methods for Automated Reservoir Simulation
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Proceedings Papers

Application of Machine Learning for Estimating Petrophysical Properties of Carbonate Rocks Using NMR Core Measurements

Ravan Farmanov, Felix Feldmann, Eric Sonny Mathew, Moussa Tembely, Emad W. Al-Shalabi, Waleed AlAmeri, Shehadeh Masalmeh, Ali AlSumaiti
Paper presented at the SPE Reservoir Characterisation and Simulation Conference and Exhibition, January 24–26, 2023
Paper Number: SPE-212625-MS
https://doi.org/10.2118/212625-MS
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View Paper titled, Application of Machine Learning for Estimating Petrophysical Properties of Carbonate Rocks Using NMR Core Measurements
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Proceedings Papers

Deep Learning Models for the Prediction of Mineral Dissolution and Precipitation During Geological Carbon Sequestration

Zeeshan Tariq, Ertugrul Umut Yildirim, Bicheng Yan, Shuyu Sun
Paper presented at the SPE Reservoir Characterisation and Simulation Conference and Exhibition, January 24–26, 2023
Paper Number: SPE-212597-MS
https://doi.org/10.2118/212597-MS
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View Paper titled, Deep Learning Models for the Prediction of Mineral Dissolution and Precipitation During Geological Carbon Sequestration
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Proceedings Papers

Super-Resolution Reconstruction of Reservoir Saturation Map with Physical Constraints Using Generative Adversarial Network

Nandita Doloi, Somnath Ghosh, Jyoti Phirani
Paper presented at the SPE Reservoir Characterisation and Simulation Conference and Exhibition, January 24–26, 2023
Paper Number: SPE-212611-MS
https://doi.org/10.2118/212611-MS
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View Paper titled, Super-Resolution Reconstruction of Reservoir Saturation Map with Physical Constraints Using Generative Adversarial Network
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Proceedings Papers

An Innovative AI Physics Hybrid Technology to Overcome the Time-Intensive History Matching Challenges, A Case Study: Minagish Oil Field

Ali Qubian, Mohammed Ahmad Zekraoui, Sina Mohajeri, Emad Mortezazadeh, Reza Eslahi, Maryam Bakhtiari, Abrar Al Dabbous, Asma Al Sagheer, Ali Alizadeh, Mostafa Zeinali
Paper presented at the SPE Reservoir Characterisation and Simulation Conference and Exhibition, January 24–26, 2023
Paper Number: SPE-212608-MS
https://doi.org/10.2118/212608-MS
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View Paper titled, An Innovative AI Physics Hybrid Technology to Overcome the Time-Intensive History Matching Challenges, A Case Study: Minagish Oil Field
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Proceedings Papers

Machine Learning Assisted Flash Calculation for Sour Gas and Crude Oil

Madiyar Koyanbayev, Lei Wang, Aset Zhumatai
Paper presented at the SPE Reservoir Characterisation and Simulation Conference and Exhibition, January 24–26, 2023
Paper Number: SPE-212673-MS
https://doi.org/10.2118/212673-MS
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View Paper titled, Machine Learning Assisted Flash Calculation for Sour Gas and Crude Oil
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Proceedings Papers

Numerical Investigation of Hybrid Smart Water and Foam Injections in Carbonate Reservoirs

Anas Mohammed Hassan, Bennet N. Tackie-Otoo, Mohammed A. Ayoub, Mysara E. Mohyaldinn, Emad W. Al-Shalabi, Imad A. Adel
Paper presented at the SPE Reservoir Characterisation and Simulation Conference and Exhibition, January 24–26, 2023
Paper Number: SPE-212663-MS
https://doi.org/10.2118/212663-MS
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View Paper titled, Numerical Investigation of Hybrid Smart Water and Foam Injections in Carbonate Reservoirs
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Proceedings Papers

Physics Informed Surrogate Model Development in Predicting Dynamic Temporal and Spatial Variations During CO 2 Injection into Deep Saline Aquifers

Zeeshan Tariq, Bicheng Yan, Shuyu Sun
Paper presented at the SPE Reservoir Characterisation and Simulation Conference and Exhibition, January 24–26, 2023
Paper Number: SPE-212693-MS
https://doi.org/10.2118/212693-MS
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View Paper titled, Physics Informed Surrogate Model Development in Predicting Dynamic Temporal and Spatial Variations During CO 2 Injection into Deep Saline Aquifers
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Proceedings Papers

Reservoir Modeling and Optimization Based on Deep Learning with Application to Enhanced Geothermal Systems

Bicheng Yan, Zhen Xu, Manojkumar Gudala, Zeeshan Tariq, Thomas Finkbeiner
Paper presented at the SPE Reservoir Characterisation and Simulation Conference and Exhibition, January 24–26, 2023
Paper Number: SPE-212614-MS
https://doi.org/10.2118/212614-MS
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View Paper titled, Reservoir Modeling and Optimization Based on Deep Learning with Application to Enhanced Geothermal Systems
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Proceedings Papers

Deep-Learning-Based Surrogate Model to Predict CO 2 Saturation Front in Highly Heterogeneous Naturally Fractured Reservoirs: A Discrete Fracture Network Approach

Zeeshan Tariq, Zhen Xu, Manojkumar Gudala, Bicheng Yan, Shuyu Sun
Paper presented at the SPE Reservoir Characterisation and Simulation Conference and Exhibition, January 24–26, 2023
Paper Number: SPE-212658-MS
https://doi.org/10.2118/212658-MS
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View Paper titled, Deep-Learning-Based Surrogate Model to Predict CO 2 Saturation Front in Highly Heterogeneous Naturally Fractured Reservoirs: A Discrete Fracture Network Approach
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Proceedings Papers

Deep Learning-Based and Kernel-Based Proxy Models for Nonlinearly Constrained Life-Cycle Production Optimization

Aykut Atadeger, Mustafa Onur, Soham Sheth, Raj Banerjee
Paper presented at the SPE Reservoir Characterisation and Simulation Conference and Exhibition, January 24–26, 2023
Paper Number: SPE-212690-MS
https://doi.org/10.2118/212690-MS
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View Paper titled, Deep Learning-Based and Kernel-Based Proxy Models for Nonlinearly Constrained Life-Cycle Production Optimization
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Proceedings Papers

Deep Recurrent Neural Network DRNN Model for Real-Time Step-Down Analysis

Srinath Madasu, Keshava Prasad Rangarajan
Paper presented at the SPE Reservoir Characterisation and Simulation Conference and Exhibition, September 17–19, 2019
Paper Number: SPE-196621-MS
https://doi.org/10.2118/196621-MS
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View Paper titled, Deep Recurrent Neural Network DRNN Model for Real-Time Step-Down Analysis
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Proceedings Papers

Visual Recognition of Drill Cuttings Lithologies Using Convolutional Neural Networks to Aid Reservoir Characterisation

Muhammad Kathrada, Benjamin Jacob Adillah
Paper presented at the SPE Reservoir Characterisation and Simulation Conference and Exhibition, September 17–19, 2019
Paper Number: SPE-196675-MS
https://doi.org/10.2118/196675-MS
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View Paper titled, Visual Recognition of Drill Cuttings Lithologies Using Convolutional Neural Networks to Aid Reservoir Characterisation
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